论文标题
在严重类不平衡下的多级分割：屋顶伤害评估中的案例研究
Multi-class segmentation under severe class imbalance: A case study in roof damage assessment
论文作者
论文摘要
屋顶损坏分类和从架空图像进行分割的任务带来了独特的挑战。在这项工作中，我们选择应对由于强烈的阶级失衡而提出的挑战。我们提出了四种旨在减轻此问题的不同技术。通过一个新方案，通过过采样少数群体和其他三个网络架构改进，将数据馈送到网络中，我们设法将模型的宏观平均F1分数提高了39.9个百分点，从而提高了细分绩效的改善，尤其是在少数群体上。
The task of roof damage classification and segmentation from overhead imagery presents unique challenges. In this work we choose to address the challenge posed due to strong class imbalance. We propose four distinct techniques that aim at mitigating this problem. Through a new scheme that feeds the data to the network by oversampling the minority classes, and three other network architectural improvements, we manage to boost the macro-averaged F1-score of a model by 39.9 percentage points, thus achieving improved segmentation performance, especially on the minority classes.
